Extracting Cognitive Impairment Assessment Information From Unstructured Notes in Electronic Health Records Using Natural Language Processing Tools: Validation with Clinical Assessment Data
Bibliographic record
Abstract
Purpose: We aimed to develop a Natural Language Processing (NLP) algorithm to extract cognitive scores from electronic health records (EHR) data and compare them with cognitive function recorded by Centers for Medicare & Medicaid Services (CMS)-mandated clinical assessments in nursing homes and home health visits. Patients and Methods: We identified a cohort of Medicare beneficiaries who had either the Minimum Data Set (MDS) or Outcome and Assessment Information Set (OASIS) linked to EHR data from the Research Patient Data Registry (Mass General Brigham, Boston, MA) from 2010 to 2019. We applied an NLP approach to identify the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE) scores from unstructured clinician notes in EHR. Using the NLP-extracted MoCA or MMSE scores from EHR, we compared mean differences of extracted MoCA or MMSE by cognition status determined by MDS (impaired vs intact cognition) and OASIS (severe impairment vs intact cognition) data, respectively. Results: Our study cohort had 7419 patients who had MDS (19.7%) or OASIS (80.3%) assessments, with a mean age of 80 (SD=7) years and 60% female. In EHR, the NLP algorithm extracted cognitive test scores with 97% accuracy (95% CI: 92-99%) for MoCA and 100% accuracy (95% CI: 84-100%) for MMSE. In MDS, the mean difference in extracted MoCA was -5.6 (95% CI: -8.7, -2.4, p=0.0008), and the mean difference in extracted MMSE was -7.9 (95% CI: -12.4, -3.5, p=0.0012). In OASIS, the mean difference in extracted MoCA and extracted MMSE was -4.8 (95% CI: -9.1, -0.6, p=0.0006) and -4.5 (95% CI: -9.5, -0.5, p=0.0182), respectively. Conclusion: We developed an NLP algorithm to accurately extract cognitive scores from unstructured EHR, and these extracted cognitive scores were well correlated with cognition function recorded in CMS-mandated clinical assessments. This could help researchers identify patients with various degrees of cognitive impairment in EHR-based research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".